IRB Protocol 80-M-0083 NCT00001174 In order to better interpret the impact of genetic variation on the brain biology of bipolar disorder, we are pursuing a variety of functional genomics studies, including brain imaging, microarray gene expression and RNA-seq in post-mortem brain tissue, and cellular phenotyping of neurons derived from induced pluripotent stem cells. Neuroimaging genetics work is focused on structural imaging as well as positron emission tomography (PET). We contributed data to the ENIGMA brain imaging consortium, which is using genome-wide association methods in large samples to detected SNPs associated with the volume of various cortical and subcortical brain regions. These important endophenotypes may shed light on the mechanisms whereby common genetic variants influence risk for a variety of psychiatric disorders. Last year, we also used magnetic resonance imaging (MRI) data from volunteers with major depressive or bipolar disorder, along with healthy volunteers, to assess the heritability of corpus callosum(CC) size and the genetic correlations among anatomical sub regions of the CC among individuals with and without mood disorders. Significant heritability was found for several sub-regions, with strong and significant genetic correlations among most sub-regions. Distinct genetic factors seem to be involved in caudal and rostral regions, consistent with the divergent functional specialization of the brain areas. We also carried out the first study to use the new technique of RNA-seq to characterize the transcribed portion of the genome (transcriptome) in post-mortem brain tissue. The resulting data were analyzed using several bioinformatics tools on high-performance computers at NIH. This study found that brain tissue from people several genes and transcripts that were involved in dysregulation of neuroplasticity, circadian rhythms and second-messenger systems in bipolar disorder. Additional ongoing analyses of these RNA-seq data are identifying patterns of RNA editing, allele-specific expression, and gene co-expression that may differ in people with bipolar disorder. We also seek to model the functional genomics of disease-related genes in cells derived from induced pluripotent stem cell (iPSC) lines. This project aims to explore the ways in which we can use iPSC technology to study the biological impact of genes and genetic mutations that we identify in our other ongoing studies. Working with the NIH Center for Regenerative Medicine and the iPSC core of NHLBI we have so far successfully reprogrammed fibroblasts from 5 different individuals; we are also studying lines reprogrammed in collaborating labs. We are differentiating the cells into neurons and glia, and characterizing their morphology, action potential, gene expression profiles, and response to medications and toxins. Careful analysis of these phenotypes could reveal differences between control and patient-derived cells, but we are also exploring ways to measure the functional impact of genetic mutations at the cellular level and to use genome editing tools such as CRISPR to rescue cellular phenotypes and establish a causal role for specific genetic mutations. This experimental system may also provide a way to characterize the biological impact of common risk alleles identified by genome-wide association studies. For example, we have recently shown that a common allele near the gene TRANK1 that has been repeatedly associated with bipolar disorder and schizophrenia leads to a significant reduction in TRANK1 gene expression in iPSc-derived neural progenitor cells that is largely reversed by chronic treatment with valproic acid at dosages that are therapeutic in people with bipolar disorder.